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       "        Unnamed: 0           user_id  first_order_time  first_order_price  \\\n",
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     "execution_count": 30,
     "metadata": {},
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   "source": [
    "#result表只有购买用户的单一数据，但是我们有全部的用户ID，所以可以重新构建一个包含购买用户和未购买用户的数据表\n",
    "import pandas as pd\n",
    "\n",
    "user2 = pd.read_csv(r\"C:\\Users\\阿璃\\Desktop\\数据分析大作业项目题目（三选一）\\B题\\数据\\result.csv\")\n",
    "user3 = pd.read_csv(r\"C:\\Users\\阿璃\\Desktop\\数据分析大作业项目题目（三选一）\\B题\\处理好的数据\\user_info1.csv\")\n",
    "user3"
   ]
  },
  {
   "cell_type": "code",
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   "id": "429ec568",
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       "                 user_id  result  Unnamed: 0  first_order_time  \\\n",
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       "135967  2000002946803184     NaN      135967   2019/5/13 12:45   \n",
       "\n",
       "        first_order_price  age_month city_num  platform_num  model_num  \\\n",
       "0                    0.00         63       杭州        9.2969     5.8824   \n",
       "1                    0.00         63       惠州        9.2969     4.2262   \n",
       "2                    9.00         63       东莞        9.2969    15.6268   \n",
       "3                    9.00         63       重庆        9.2969    13.8517   \n",
       "4                    0.00         63       毕节        9.2969     1.5464   \n",
       "...                   ...        ...      ...           ...        ...   \n",
       "135963               0.00         63       徐州       13.5570    10.8966   \n",
       "135964               0.00         63       保定       13.5570     8.1782   \n",
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       "135966               0.00         24       泉州        9.2969    15.3061   \n",
       "135967               0.01         45    error        9.2969     8.8308   \n",
       "\n",
       "        app_num  \n",
       "0             1  \n",
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   "source": [
    "#使用外连接拼接，保留全部用户ID，没有下单的用户‘result’列表示为空缺值\n",
    "merged_data = pd.merge(user2,user3,on = 'user_id',how='outer')\n",
    "merged_data"
   ]
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  {
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   "id": "f93a7ea6",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135965</th>\n",
       "      <td>2000002945866461</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135966</th>\n",
       "      <td>2000002945873156</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135967</th>\n",
       "      <td>2000002946803184</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135968 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 user_id  result\n",
       "0       2000001563151338     1.0\n",
       "1       2000001563163750     1.0\n",
       "2       2000001563266119     1.0\n",
       "3       2000001566046975     1.0\n",
       "4       2000001566153564     1.0\n",
       "...                  ...     ...\n",
       "135963  2000002945827404     NaN\n",
       "135964  2000002945862051     NaN\n",
       "135965  2000002945866461     NaN\n",
       "135966  2000002945873156     NaN\n",
       "135967  2000002946803184     NaN\n",
       "\n",
       "[135968 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = merged_data.loc[:, 'user_id':'result']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cc6fd982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame._add_numeric_operations.<locals>.any of         user_id  result\n",
       "0         False   False\n",
       "1         False   False\n",
       "2         False   False\n",
       "3         False   False\n",
       "4         False   False\n",
       "...         ...     ...\n",
       "135963    False    True\n",
       "135964    False    True\n",
       "135965    False    True\n",
       "135966    False    True\n",
       "135967    False    True\n",
       "\n",
       "[135968 rows x 2 columns]>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0cb2498a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#将空缺值填充为0\n",
    "data.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a1f6e31c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率: 0.9641832757225859\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "X = data.drop(\"result\", axis=1)  # 特征列\n",
    "y = data[\"result\"]  # 目标列\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "model = RandomForestClassifier()  # 随机森林分类器\n",
    "model.fit(X_train, y_train)  # 训练模型\n",
    "y_pred = model.predict(X_test)  # 预测结果\n",
    "accuracy = accuracy_score(y_test, y_pred)  # 计算准确率\n",
    "print(\"准确率:\", accuracy)\n",
    "predictions = model.predict_proba(X_test)[:, 1]  # 预测下单购买的概率\n",
    "output = pd.DataFrame({'用户ID': X_test['user_id'], '预测下单购买概率': predictions})\n",
    "output.to_csv(\"sample_output1.csv\", index=False)  # 输出为 CSV 文件\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0343f0e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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